Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease.
Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease.
复制标题
实时心血管MR具有时空伪影抑制，使用先天性心脏病中的概念深度学习。
DOI：
10.1002/mrm.27480
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发表时间：
2019-03
影响因子：
3.3
通讯作者：
Steeden JA
中科院分区：
文献类型：
--
作者：
Hauptmann A;Arridge S;Lucka F;Muthurangu V;Steeden JA
心室容积的真实的实时评估需要高加速因子。残差卷积神经网络（CNN）已经显示出消除数据欠采样造成的伪影的潜力。在这项研究中，我们研究了CNN在先天性心脏病（CHD）患者中重建高度加速径向真实的时间数据的能力。开发了3D（2D加时间）CNN架构，并使用从250名CHD患者先前采集的屏气电影图像创建的合成训练数据进行训练。然后使用经过训练的CNN重建在10名新的CHD患者中采集的实际真实的实时微小黄金角（TGA）径向SSFP数据（13倍欠采样）。同样的真实的实时数据也用压缩感知（CS）重建，以比较图像质量和重建时间。将使用CNN和CS重建图像进行的心室容积测量与参考标准屏气数据进行比较。训练CNN以从高度欠采样的径向真实的时间数据中去除伪影是可行的。CNN的总体重建时间（包括创建混叠图像）比CS重建快5倍以上。此外，从CNN重建图像测量的双心室容积的图像质量和准确性上级CS重建。本文证明了在临床环境中使用CNN重建真实的实时放射状数据的潜力。使用CNN重建的真实的时间数据进行心室容积的临床测量与金标准、心脏门控、屏气技术无统计学显著差异。
Real‐time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts caused by data undersampling. In this study, we investigated the ability of CNNs to reconstruct highly accelerated radial real‐time data in patients with congenital heart disease (CHD). A 3D (2D plus time) CNN architecture was developed and trained using synthetic training data created from previously acquired breath hold cine images from 250 CHD patients. The trained CNN was then used to reconstruct actual real‐time, tiny golden angle (tGA) radial SSFP data (13 × undersampled) acquired in 10 new patients with CHD. The same real‐time data was also reconstructed with compressed sensing (CS) to compare image quality and reconstruction time. Ventricular volume measurements made using both the CNN and CS reconstructed images were compared to reference standard breath hold data. It was feasible to train a CNN to remove artifact from highly undersampled radial real‐time data. The overall reconstruction time with the CNN (including creation of aliased images) was shown to be >5 × faster than the CS reconstruction. In addition, the image quality and accuracy of biventricular volumes measured from the CNN reconstructed images were superior to the CS reconstructions. This article has demonstrated the potential for the use of a CNN for reconstruction of real‐time radial data within the clinical setting. Clinical measures of ventricular volumes using real‐time data with CNN reconstruction are not statistically significantly different from gold‐standard, cardiac‐gated, breath‐hold techniques.
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影响因子：
5.4
作者：
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通讯作者：
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